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» An Instance Selection Approach to Multiple Instance Learning
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ICPR
2004
IEEE
14 years 11 months ago
Selective Sampling Based on the Variation in Label Assignments
In this paper, a new selective sampling method for the active learning framework is presented. Initially, a small training set ? and a large unlabeled set ? are given. The goal is...
Piotr Juszczak, Robert P. W. Duin
JMLR
2006
99views more  JMLR 2006»
13 years 10 months ago
Worst-Case Analysis of Selective Sampling for Linear Classification
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...
GECCO
2006
Springer
214views Optimization» more  GECCO 2006»
14 years 1 months ago
A new discrete particle swarm algorithm applied to attribute selection in a bioinformatics data set
Many data mining applications involve the task of building a model for predictive classification. The goal of such a model is to classify examples (records or data instances) into...
Elon S. Correa, Alex Alves Freitas, Colin G. Johns...
ICDM
2005
IEEE
134views Data Mining» more  ICDM 2005»
14 years 3 months ago
A Preference Model for Structured Supervised Learning Tasks
The preference model introduced in this paper gives a natural framework and a principled solution for a broad class of supervised learning problems with structured predictions, su...
Fabio Aiolli
COLING
2010
13 years 4 months ago
Maximum Metric Score Training for Coreference Resolution
A large body of prior research on coreference resolution recasts the problem as a two-class classification problem. However, standard supervised machine learning algorithms that m...
Shanheng Zhao, Hwee Tou Ng